QUALITY BEFORE QUANTITY: QUALITATIVE CONTENT ANALYSIS MADE EASY

Welcome to the tutorial “Qualitative content analysis in simple terms – 10 facts you should know“. Qualitative content analysis is a method for collecting, analyzing and interpreting qualitative data. In the context of theses, it is often used to evaluate qualitative interviews. If you too are planning to make use of this method in your thesis, but are not yet familiar enough with qualitative content analysis, then you have come to the right place. In this tutorial, we will briefly and clearly explain everything you need to know about qualitative content analysis – its goal, its benefits, forms of application, and the criteria for its successful implementation.

Qualitative content analysis - what's it all about?

Let's start with the goal of qualitative content analysis. Qualitative content analysis is about evaluating the content of data material systematically and in detail in order to answer a research question. The evaluation is carried out on the basis of a system of categories, which is intended to represent the most important text contents in a condensed form.

Any input that is available in text form can become the object of qualitative content analysis. The spectrum ranges from newspaper articles and songs to TV and radio reports and research reports. Thus, this method offers the benefit of flexibility, since you can apply it to different types of content: In addition to the analysis of texts, it is also suitable for tapping into other communication material in terms of its content.

However, its flexibility is not the only benefit. In addition, qualitative content analysis guarantees a certain degree of reliability. Therein lies its special feature compared to other methods of qualitative research. This is because, as we will see in more detail, it satisfies scientific quality criteria and can thus, in an optimum situation, deliver comprehensible, verifiable and usable results.

Finally, quantitative content analysis is also compatible with a quantitative evaluation procedure insofar as the qualitative data can be evaluated statistically. Thus, it contributes to bridging the divide between qualitative and quantitative methods and enables further research strategies.

The 3 types of qualitative content analysis

Once you have decided which data material best fits your research question and the direction of your analysis has been determined, it is time to select an appropriate form of content analysis. In total, there are three forms to choose from: summarizing content analysis, explicating content analysis, and structuring content analysis.

The purpose of summarizing content analysis is to reduce a given amount of text material to its essential content. The text processing takes place through several process steps, namely paraphrases, generalizations, and reductions.

Paraphrasing is about unifying the data material thematically, removing embellishments and irrelevant text elements.

Generalization aims at further abstracting the paraphrased content and subsuming it under keyword-like terms.

Reduction involves deleting paraphrases that have the same meaning and merging paraphrases that are similar in content.

Explicating content analysis is used to make unclear passages in the text in question more comprehensible. On the one hand, sentences in the text environment can be used for this purpose, insofar as they serve to explain the text. On the other hand, it is possible to use additional material. This includes external works such as encyclopedias and literature sources.

While summarizing and explicating content analysis serve to process the text material before it can be coded, structuring content analysis ties in with material that has already been processed. In structuring content analysis, the goal is to filter out a certain structure from the text material. The structuring is done according to certain formal criteria, which you must derive from the relevant literature. The starting point for the analysis is a predefined system of categories. First, you determine exactly which text elements should fall under which categories. Then you identify representative text passages i.e. quotations for each category. In order to ensure that a thematically related text passage is only assigned to a single category in the case of delimitation problems, the last step is to define coding rules. This is done within the framework of a coding guideline, in which it is regulated under which conditions text contents are to be categorized and how.

The 3 quality criteria in qualitative research

In order for the results of your research to be usable and trustworthy, you must adhere to the quality criteria of qualitative research. Even though, unlike quantitative research, there are no standardized quality criteria, it makes sense to adhere to the following three criteria: transparency, intersubjectivity, and reach.

You ensure transparency by documenting the entire research process in detail and explaining it step by step. This includes, among other things, the formulation of the central question, the description and justification of the methods as well as the explanation of the data evaluation and interpretation. In this way, your procedure becomes comprehensible to external parties.

The intersubjectivity of your research is present when you reflect on your subjectively obtained data, put it up for discussion and offer different possibilities of interpretation. In this way, you do not present your point of view as the only correct one and ensure that others can form their own opinion.

The criterion of reach is about ensuring that comparable results could be obtained if a similar analysis was repeated. A qualitative content analysis only proves to be reliable and practically relevant if your category system and coding guide lead to the same results when used again on the same data material.

A useful tool in this context is the so-called intercoder reliability. In order to determine the reliability of the results, a procedure is used in which several coders carry out the same analysis independently of each other. If there are distortions in the results, the category system or the definition of the categories must be optimized, and a further run follows.

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